解决车联网中车辆数据异构稀疏问题，提升模型预测精度
Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks
- 分层联邦迁移学习，按车类型聚类协同训练
- 在真实数据集上实现更高全局模型准确率
- 支持不同车型适配，适合智能交通系统应用
在基于数字孪生的车载自组网（DT-VANET）研究中，联邦学习（FL）展现出保护数据隐私的能力。然而，当面对车辆间的数据异构性与数据稀疏性时，联邦学习难以充分训练全局模型，导致对不同车型的精确预测性能不佳。为此，本文结合联邦迁移学习（FTL），针对车辆类型进行聚类，并提出一种新型分层联邦迁移学习（HFTL）框架。构建了适用于DT-VANET的系统架构，设计了云端模型更新算法与簇内联邦迁移学习算法，有效提升了全局模型的准确性。此外，提出基于数据质量评分的机制，防止恶意车辆影响全局模型。最后，在真实数据集上开展详尽实验，通过多种性能指标验证了算法的有效性与高效性。
原文摘要 · Abstract (English)
In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types. To address these challenges, this paper combines Federated Transfer Learning (FTL) to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning (HFTL). We construct a framework for DT-VANET, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model. In addition, we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles. Lastly, detailed experiments on real-world datasets are conducted, considering different performance metrics that verify the effectiveness and efficiency of our algorithm.
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